This invention relates in general to an automated method for the quality assurance (QA) of x-ray digital radiography imaging systems. More particularly, it relates to such a method that can provide a simple and quick way to quantitatively measure the characteristics of storage phosphor-based computed radiography (CR) imaging systems and direct-digital flat-panel detector-based direct radiography (DR) imaging systems.
There are a number of important parameters that together characterize the performance of x-ray imaging systems. For CR and DR, most of these parameters are common to both, yet some are unique to the particular type of system. Those common parameters include spatial resolution, noise, detective efficiency, exposure response, dark image signal level, and artifacts etc.
For CR and DR, the modulation transfer function (MTF) of the imaging system is often used to characterize the system spatial resolution. MTF is a 2D (two-dimensional) function of spatial frequency and is usually measured for both x and y directions of the acquired image. There have been developed three major types of techniques for MTF measurement: resolution target, angular slit and angular edge. The resolution target technique depends on imaging either a commercially available bar-target or a star-pattern target (J. Anthony Seibert, xe2x80x9cPhotostimulable Phosphor System Acceptance Testing,xe2x80x9d AAPM Medical Physics Monograph No. 20: Specification, Acceptance Tesing and Quality Control of Diagnostic X-ray Imaging Equipment, 1991) or a custom-made bar target of varying resolution (J. Daniel Newman, Daniel K. McBridge, James C. Montoro, xe2x80x9cAutomatic Technique for Calibrating A Storage Phosphor Reader,xe2x80x9d U.S. Pat. No. 5,420,441, 1995). In this technique, the MTF is estimated either using a human observer to identify the blur frequency point of the target or calculating the visibility modulation. The error of the estimation depends on the orientation/resolution of the target and the subjective criteria of the observer. It requires that he target be perfectly aligned in the x or y direction, and the severity of the error increases when the target resolution is close to the Nyquist frequency of the imaging system.
Another method that makes use of a narrow slit can be used to measure the line spread function, followed by Fourier transformation to obtain the MTF of the imaging system in the slit transverse direction (Hiroshi Fujita, Du-Yih Tsai, Takumi Itoh, Kunio Doi, Junji Morishita, Katsuhiko Ueda, and Akiyoshi Ohtsuka, xe2x80x9cA Simple Method for Determining the Modulation Transfer Function in Digital Radiography,xe2x80x9d IEEE Transactions on Medical Imaging, Vol. 11, No. 1, 1992). In this technique, to obtain the MTF in the x or y direction, the orientation of the slit must be placed at a slight angle with the y or x direction in order to achieve super sampling for aliasing reduction. The width of the slit is required to be much narrower than the sampling pitch (pixel size) of the imaging system, and the slit needs to be long enough to cover at least one pixel in the slit transverse direction. Although this is a very accurate method for MTF measurement, it relies on a delicately made, expensive slit target.
A third method makes use of a sharp and straight edge target to measure the edge spread function of the imaging system. The MTF in the edge transverse direction can be obtained from the edge spread function by taking the Fourier transform of it""s derivative (Stephen E. Reichenbach, Stephen K. Park, and Ramkumar Narayanswamy, xe2x80x9cCharacterizing Digital Image Acquisition Devices,xe2x80x9d Optical Engineering, Vol. 30, No. 2, 1991). Because sharp and straight edges are relatively easy to manufacture accurately, this method is preferred for MTF measurement for the quality assurance of digital radiography imaging systems.
The noise of the imaging system determines the system low-contrast resolution as well as the x-ray detective efficiency etc. The noise characteristics can be described by the noise power spectrum (NPS) of the imaging system, which is also a 2D function of spatial frequency. To obtain the NPS, a flat image region is usually taken for Fourier analysis. Because the system noise level is also x-ray exposure dependent, the NPS is often measured at a certain exposure level to facilitate comparisons among imaging systems.
Detective efficiency is a secondary parameter of the imaging system that can be readily calculated from the system MTF and NPS:       DQE    ⁡          (              u        ,        v            )        =            DQE      ⁡              (                  0          ,          0                )              ⁢                                        MTF                                          xe2x80x83                            ⁢              2                                ⁡                      (                          u              ,              v                        )                                    NPS          ⁡                      (                          u              ,              v                        )                              .      
Exposure response describes the relationship between the output of the imaging system (image pixel values) and the incident x-ray exposure. Ideally, the exposure response or logarithmic exposure response should be linear and equal for all the pixels across the whole image. Response accuracy, linearity, and uniformity are three of the major parameters for exposure response characterization. Exposure accuracy and linearity describes how accurately and linearly the output of the imaging system can track the incident x-ray exposure. Response uniformity describes the inter-pixel response variation. All three parameters are usually measured using the same x-ray spectrum but different exposure levels.
The dark image signal level determines the baseline noise of the imaging system and it is independent of x-ray exposure. For a CR image, this corresponds to the signal level that would result from reading an erased phosphor screen, and for a DR image, this corresponds to the accumulated noise level before the x-ray exposure and during the readout process.
Artifacts may also be present in images. The nature of artifacts is that they are often unpredictable and may take the form of spots, lines and low-frequency modulations etc and can also be periodic or non-periodic. The white and dark spots are usually caused by foreign dust/dirt residing on the image receptor or caused by the bad pixels (DR). There are two major types of line artifacts, periodic (banding), and non-periodic (streaks). Either artifact will result in objectionable image quality if the magnitude is large enough.
There are several other important parameters for image QA that are unique to either CR or DR. For a CR imaging system, a laser beam is used for raster scanning and reading the signal from the storage phosphor screen. Because there are moving optical devices, the image pixel size and the pixel aspect ratio can be spatially variant. The other two important parameters for CR image quality assurance are scan linearity and scan accuracy which are measures of the geometric integrity of the image. Although there is no variable geometry related quality assurance issues for DR because all the imaging pixels are solid state elements manufactured on an evenly distributed grid, the locations of failed pixels and the individual pixel response correction are unique to DR and need to be characterized.
The large number of diversified parameters to be measured for the QA of digital x-ray imaging systems, presents a considerable challenge for designing a quick, accurate, easy to use, and fully automatic method/procedure to conduct the measurements. There have been considerable efforts so far in this area. However, most of the proposed methods rely either on visually reading image pixel values from a computer screen or on printing a test image on film and then using visual examination combined with film densitometer measurements. For example, the methods using film prints and densitometer measurements include (1) the work by J. Anthony Seibert (xe2x80x9cPhotostimulable Phosphor System Acceptance Testing,xe2x80x9d AAPM Medical Physics Monograph No. 20: Specification, Acceptance Testing and Quality Control of Diagnostic X-ray Imaging Equipment, 1991), (2) the work by Walter Huda, Manuel Arreola and Xhenxue Jing (xe2x80x9cComputed Radiography Acceptance Testing,xe2x80x9d SPIE Symposium on Medical Imaging 1995, Vol. 2432), (3) the work by Hamid Jafroudi, Dot Steller, Matthew Freeman and Seong Ki Mun (xe2x80x9cQuality Control on Storage Phosphor Digital Radiography Systems,xe2x80x9d SPIE Symposium on Medical Imaging 1995, Vol. 2432), (4) and the several methods by x-ray test target manufacture companies. The latter include the EZ CR Test Tool from Nuclear Associate (100 Voice Raod, Carie Place, N.Y. 11514-0349) and the X-Ray Test Objects for Computed Radiography from FAXiL (Wellcome Wing, Leeds General Infirmary, Great George St., Leeds LS1 3EX, West Yorks, England). However, all these provided methods are limited by the quality of the film printer and densitometer.
Kodak provides a recommendation for the CR QC procedure (Terese M. Bogcki, xe2x80x9cAcceptance Testing And Quality Control of Computed Raiography Systems,xe2x80x9d Technical and Scientific Bulletin No. 8792038, Eastman Kodak Company, 1997). Although this method uses direct image pixel value reading from the quality control workstation (QCW), it is not automatic and still relies on film prints for many of the measurements. The AAPM Task Group No. 10 summarized the testing methods provided by three major CR manufacturers, Kodak, Fuji and Agfa. Some results using the AAPM recommendations were published (xe2x80x9cAcceptance testing of Computed Radiography Imaging Systemsxe2x80x94An Update,xe2x80x9d Scientific Exhibit on the Radiology Society of North America"" 1998). However, the proposed method itself is still not automatic and is tedious to conduct.
Only a few automated methods have been proposed so far for the QA of digital x-ray imaging systems. For example, the method proposed by J. Daniel Newman, Daniel K. McBridge and James C. Montoro (xe2x80x9cAutomated Technique for Calibrating A Storage Phosphor Reader;xe2x80x9d U.S. Pat. No. 5,420,441, 1995) is based on imaging a specially made phantom and analysis of the phantom image to derive the system performance parameters. These derived parameters are then compared with those corresponding to pre-stored threshold values of a normal imaging system. Decisions are made regarding the system performance based on the comparisons. Similar approaches are also reported by Roland Reitan (xe2x80x9cAutomated Image Quality Control,xe2x80x9d PCT/US95/31869) and Agfa (xe2x80x9cADC Auto Software Modulexe2x80x9d). Another approach disclosed by Luc Grillet (xe2x80x9cPhotostimulable Imaging Plate And Method of Testing A Digital Device for Scanning Such A Plate,xe2x80x9d U.S. Pat. No. 5,591,968, 1997) uses a phosphor screen on which a special mask pattern is printed for x-ray exposure. The mask pattern is transparent to x-rays but not to the laser beam used in the readout process. Therefore, the latent x-ray signal from the exposure can be read only at location on the phosphor screen where there is no mask pattern. A phantom image is created by this means and the analysis is conducted thereafter. All aforementioned methods, however, have only limited accuracy for MTF measurement because the measurement is derived using a bar-target.
There was proposed use of the angular edge method instead of the bar-target for MTF measurement (Kenneth A. Fetterly, Ramesh Avula and Nicholas J. Hangiandreou, xe2x80x9cDevelopment And Implementation of An Automated, Quantitative Film Digitizer Quality Control Program,xe2x80x9d SPIE Symposium on Medical Imaging 1999), an (Ehsan Samei and Michael J. Flynn, xe2x80x9cA Methodfor Measuring The Presampled MTF of Digital Radiographic Systems Using An Edge Test Devicexe2x80x9d, Medical Physics 25(1), January 1998). However, the first method was designed for the film digitizer application only and will be ineffective for CR system performance evaluation. Because the phantom is made by the film printer, its performance is limited by the printer. The second method, however, does not teach how to conduct the measurement automatically.
This invention relates in general to an automated method for the quality assurance (QA) and quality control (QC) of x-ray digital radiography imaging systems. More particularly, it relates to such a method that can provide a simple and quick way to quantitatively measure the characteristics of storage phosphor-based computed radiography (CR) imaging systems and direct-digital flat-panel detector-based direct radiography (DR) imaging systems.
According to a feature of the present invention, there is provided a phantom for use in measuring parameters in a digital radiography image system comprising:
a substantially flat member of an x-ray attenuating material;
a first array of landmarks associated with said member for use in geometry measurements;
a set of regions associated with the central portion of said member for exposure linearity and accuracy measurement; and
a set of sharp angular edges part of one of said regions for modulation transfer function measurements.
According to another feature of the present invention, there is provided in a digital radiographic imaging system including a variable source of x-rays, an x-ray image receptor, and a digital image processor, a method of measuring pre-selected parameters of said system comprising:
erasing the image photoreceptor and exposing said image receptor to a flat field x-ray image at a predetermined x-ray exposure level from said x-ray source to produce a digital flat field x-ray image;
erasing the image receptor immediately after a relatively high x-ray exposure level from said x-ray source followed by reading said image receptor to produce a digital dark erased x-ray image;
exposing said image receptor to x-rays from said x-ray source projected through a phantom having regions of different x-ray attenuation to produce a digital phantom x-ray image; and
processing in said digital image processor said digital x-ray images to determine parameters of said digital radiographic imaging system.
The invention has the following advantages.
(1) The defined procedure to acquire images is simple and quick to conduct, which helps to reduce system downtime.
(2) The software for image analysis is fully automated. It is fast in computation and can provide quantitative and reliable measurements of system characteristics. The measurements are precise and sensitive.
(3) Eliminates all subjective assessments and third-party measurement device dependence.
(4) The procedure/method makes it possible that one standard can be used by the manufacture, field service and hospital.
(5) The specially designed cassettes holders allow the same target to be used for many cassette types.